[R-sig-ME] Interpretation of lme output with correlation structure specification

[Andrew Robinson]

Hi Udita,

You should read the book cited in the package. It’s really worthwhile.

Best wishes,

Andrew

--
Andrew Robinson
Director, CEBRA, School of BioSciences
Reader & Associate Professor in Applied Statistics Tel: (+61) 0403 138 955
School of Mathematics and Statistics Fax: (+61) 03 8344 4599
University of Melbourne, VIC 3010 Australia
Email: apro using unimelb.edu.au
Website: http://cebra.unimelb.edu.au/
On 12 Aug 2018, 7:34 AM +1000, Bansal, Udita <udita.bansal17 using imperial.ac.uk>, wrote:
> Hi all,
>
> I was modeling the laying date of bird nests against moving averages of weather variables for several years of data. I used Durbin-Watson test and found considerable amount of autocorrelation in the residuals of simple linear and mixed effect models (with month as a random factor). So, I decided to run lme models with correlation structure specified. When I compare the AIC of the models with and without the correlation structure, I find that the models with the correlation structure are better.
> Question 1.: How can I interpret the phi (parameter estimate for correlation structure) value in the model output?
> Question 2.: Does the interpretation of phi affect the interpretation of the random effect?
> Question 3.: How can I interpret the random effect (since this is different from what lmer output shows which I am used to of)?
>
> An example output is as below:
>
> Random effects:
> Formula: ~1 | month
> (Intercept) Residual
> StdDev: 12.53908 5.009051
>
> Correlation Structure: AR(1)
> Formula: ~1 | month
> Parameter estimate(s):
> Phi
> 0.324984
>
> I could not find much on the interpretation for these online. Any help will be much appreciated.
>
> Thanks
> Udita Bansal
>
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>
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